Human Activity Recognition, or HAR, is a field of study to understand, recognize, and predict human activities through machine learning (ML). In HAR, raw time-series data from wearable sensors are acquired and translated to human activities (e.g. walking, sitting, jumping, fighting, falling, etc.).
Thanks to the ubiquity of wearable sensors and the Internet of Things (IoT) and their ever-growing computing, networking, and sensing powers, designing and building effective and efficient wearable-sensor-based human activity recognition (HAR) systems have gained increased attention from researchers and tech industries in the last decade. Apart from the various wearable sensor-based HAR systems proposed in the literature, various commercial wearable products are now available on the market including fitness trackers, smartwatches, and smartphones which are packed with various wearable sensors. Commonly utilized wearable sensors in HAR systems[1]} include accelerometer[2]}, magnetometer, gyroscopes, inertial measurement unit (IMU), electromyogram (EMG)[3]}, force-sensitive resistors (FSR)[4]} and wearable wrist camera[5]}.
The wearable sensors-driven HAR has critical applications in healthcare [6]}, [7]}, [8]}, [9]}, human-robot interaction [10]}, [11]}, interactive gaming [4]}, sports [13]}, [2]}, military [15]}, [16]} and so on.
